Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities
It is often desired that ordinal regression models yield unimodal predictions. However, in many recent works this characteristic is either absent, or implemented using soft targets, which do not guarantee unimodal outputs at inference. In addition, we argue that the standard maximum likelihood objec...
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| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
15-11-2020
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | It is often desired that ordinal regression models yield unimodal
predictions. However, in many recent works this characteristic is either
absent, or implemented using soft targets, which do not guarantee unimodal
outputs at inference. In addition, we argue that the standard maximum
likelihood objective is not suitable for ordinal regression problems, and that
optimal transport is better suited for this task, as it naturally captures the
order of the classes. In this work, we propose a framework for deep ordinal
regression, based on unimodal output distribution and optimal transport loss.
Inspired by the well-known Proportional Odds model, we propose to modify its
design by using an architectural mechanism which guarantees that the model
output distribution will be unimodal. We empirically analyze the different
components of our proposed approach and demonstrate their contribution to the
performance of the model. Experimental results on eight real-world datasets
demonstrate that our proposed approach consistently performs on par with and
often better than several recently proposed deep learning approaches for deep
ordinal regression with unimodal output probabilities, while having guarantee
on the output unimodality. In addition, we demonstrate that proposed approach
is less overconfident than current baselines. |
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| DOI: | 10.48550/arxiv.2011.07607 |